Method and system for determining fact of visit of user to point of interest

ABSTRACT

A method of determining a conversion rate of a targeted message associated with a point of interest (POI) includes determining a plurality of users using wireless devices that have been exposed to a targeted message associated with the POI within a first pre-determined time period and receiving a plurality of sets of local area communication features of the local area communication modules of the wireless devices. These features are inputted into a Machine Learning Algorithm (MLA) trained using a set of heuristics applied to training sets of local area communication features of training wireless devices. The MLA outputs, based on the set of local area communication features of the local area communication module of the wireless device, a plurality of indications of the conversion rate of the targeted message. A server for executing the method is also provided.

CROSS-REFERENCE

The present application is a Divisional application of a U.S. patentapplication Ser. No. 16/573,195, filed on Sep. 17, 2019, entitled“METHOD AND SYSTEM FOR DETERMINING FACT OF VISIT OF USER TO POINT OFINTEREST”, which, in turn, claims priority to a Russian PatentApplication No. 2018/146,463 bearing the same title, filed on Dec. 26,2018. The entirety of both is incorporated herein by reference.

FIELD

The present technology relates to computer implemented methods andsystems for determining facts of visits of users to points of interests.

BACKGROUND

Wireless devices, such as smartphones and Wi-Fi™ enabled tablets areubiquitously used by users. Many such wireless devices include one ormore geo-location systems, such as one or more Global PositioningSystems (GPS), that enable the wireless devices to determine their owngeo-location.

For various uses and applications, it may be desirable to determine fromthe geo-locations of users, either in real time or by processingexisting geo-track data provided by the users' wireless devices, whenthe users are visiting various Points of Interest (POIs). Examples ofPOIs may include parks or other attractions, businesses, privateresidences, and so on. In some cases, it may be desirable to determine,from the geo-locations of users, either in real time or by processingexisting geo-track data provided by the users' wireless devices, thename and/or other information relating to each particular POI the usersare visiting.

The prior art geo-location systems, including the geo-location systemsof the wireless devices themselves, are suitable for such purposes atleast in some cases. For example, many prior art geo-location systemsare typically suitable to determine when a given user is visiting alarge park that has few or no other POIs nearby. In such a situation, agiven wireless device may determine its geo-location with a relativelyhigh level of certainty, compared to when the same device may be locatedin a highly dense urban geographical area having a large number of POIsnearby.

In other circumstances, however, prior art geo-location systems may beinaccurate, and in some particular cases simply non-usable. For example,the denser a given geographical area visited by a user is in terms ofthe number of POIs in the area, the less reliable prior art geo-locationsystems tend to be due at least in part to increasing uncertaintiesrelated to the accuracy of the geo-locations provided by suchgeo-location systems and due to an increasing number of possible POIsthat a user may be visiting in any given geo-location.

Some geo-location systems have been developed to improve upon at leastsome of the drawbacks described above.

For example, U.S. Pat. No. 9,639,858 B2, entitled “SYSTEMS AND METHODSTO ATTRIBUTE REAL-WORLD VISITS OF PHYSICAL BUSINESS LOCATIONS BY A USEROF A WIRELESS DEVICE TO TARGETED DIGITAL CONTENT OR PUBLICLY DISPLAYEDPHYSICAL CONTENT PREVIOUSLY VIEWABLE BY THE USER”, and assigned toFacebook Inc., teaches methods and systems that record the location of auser and transmit targeted content to a user based upon their currentand past location information. A network is configured to include aserver programmed with a database of targeted content, a database oflocation information, a database of user information, a databasesearching algorithm, and a wireless communication system capable ofcommunicating with the user's mobile device. The location of the mobiledevice is ascertained and recorded. The location information is analyzedto determine the routes taken by the user, businesses visited by theuser, and other behaviors of the user. Targeted content is sent to themobile device of the user or exposure to physical content is tracked.Whether the user visits the physical locations associated with thecontent is monitored. Detailed conversion tracking is provided toproducers of targeted content and business owners.

As another example, U.S. Pat. No. 9,135,655 B2, entitled “SYSTEMS ANDMETHODS FOR USING SERVER SIDE COOKIES BY A DEMAND SIDE PLATFORM”, andassigned to MediaMath Inc., teaches methods for identifying a user by ademand side platform (DSP) across advertiser exchanges. The methodincludes establishing, by a DSP, a cookie mapping for a user. The cookiemapping includes a mapping of user identifiers for the user fromadvertisement exchanges to a user identifier assigned by the DSP for theuser. The DSP stores to the cookie mapping a first mapping to the useridentifier of the DSP, comprising a first user id received by a bidderfrom a first exchange and a first exchange id for the first exchange. Abidder inserts a pixel into a bid for an impression opportunity to asecond exchange. The pixel includes a key to the cookie mapping and asecond user id for the user and a second exchange id. The second user idis received by the bidder from a second exchange.

As yet another example, Patent Application Number US 2009/0234745 A1,entitled “METHODS AND SYSTEMS FOR MOBILE COUPON TRACKING”, and assignedto Millennial Media LLC, teaches a method and system for presenting asponsored mobile coupon to a mobile communication facility based atleast in part on a relevancy, wherein the relevancy is based at least inpart on a mobile subscriber characteristic, redeeming the coupon at anoffline sponsor location using the mobile communication facility,recording conversion of the coupon in a conversion data repository,transmitting the conversion data repository to a wireless carrier, andanalyzing the conversion data repository to determine an action.

While these other prior art systems and methods described above may besuitable for at least some of their intended purposes, they nonethelesshave their own drawbacks.

SUMMARY

The present technology has been developed with a view to improving atleast upon some of the drawbacks of prior art geo-location systems forat least some particular applications.

In summary, the present technology provides methods and systems fordetermining facts of visits by identified, or identifiable, users toidentified, or identifiable, points of interest (POIs). The methods andsystems use a particularly configured Machine Learning Algorithm (MLA)which determines the facts of visits of a given one or more users basedon the Wi-Fi and/or other wireless networks detected by the given one ormore users' wireless devices. In some particular non-limitingembodiments of the present technology, the systems include a server thatis used to develop a particular training dataset and train the MLA usingthe particular training dataset.

In some such embodiments, the server executes a metrics module, a POIfingerprint module, a feature engineering module, a training datasetgenerating module, and an MLA training module. Each of these modules isdescribed in detail in later in this document. At least one non-limitingembodiment of each of the modules is summarized herein next. It shouldbe understood that while a particular set of modules is described inthis document, the functionality achieved thereby may be achieved usinga different set of modules and/or configurations of the server.

In summary, the metrics module accesses wireless devices of users in aparticular geographical area of interest, and obtains timestamped localarea network (LAN) snapshots from each of these wireless devices over aperiod of time. These snapshots represent Wi-Fi networks detected by thewireless devices at particular times and geo-locations. From each of theobtained LAN snapshots, the metrics module removes dynamic and mobileLANs, leaving only information about static LANs. The metric modulethereby removes less reliable features/indicators/data from the LANsnapshots.

In summary, the POI fingerprint module uses a subset of the LANsnapshots determined by the metrics module to generate at least one POIfingerprint for each POI in the geographical area of interest, which POIwas visited at least once over the pre-determined timeframe. To thisend, the POI fingerprint module obtains timestamped geo-tracks of thewireless devices of the users and applies to them a set of heuristicsthat determines facts and times of visits of the users to the POIs inthe geographical area. For each determined fact of visit of a particularuser having a particular wireless device, the POI fingerprint moduleretrieves the LAN snapshot as detected by that particular wirelessdevice at the time of the determined fact of visit.

The POI fingerprint module thereby generates, for eachat-least-once-visited POI in the geographical area, at least one, and inmany cases, a plurality of LAN snapshots captured from “within” theat-least-once-visited POI. The POI fingerprint module filters out noisefrom the obtained LAN snapshot sets using known statistical techniques,and amalgamates each set of LAN snapshots for a given POI into a singlePOI fingerprint of that given POI. Thus, in a sense, the POI fingerprintof a POI represents, inter alia, a typical set of Wi-Fi's and theircharacteristics that may be expected to be detected by a wireless device“visiting” that POI. In some embodiments, a statistics module furtherdevelops additional metrics and features associated with each of the POIfingerprints.

Next, the feature engineering module executes various comparisons of thedata obtained by the abovementioned modules, and identifies features ofthe given POI fingerprint that distinguish visits to a first POI fromvisits to a second POI. A training dataset is then inputted by an MLAtraining module into the MLA to train the MLA.

In use, the trained MLA may receive as input a given LAN snapshot asdetected by a given wireless device of a given user at a given time. Insome cases, the MLA may receive a series of such inputs, correspondingto different times at which the given wireless device reported itsgeo-location. In some cases, the series of inputs may correspond to theuser of the wireless device moving around in the geographical area ofinterest. For each such input, the MLA determines whether or not theuser is visiting a given one of the POIs in the geographical area at thetime associated with the input.

With the above summary in mind, and according to a first aspect of thepresent technology, there is provided a method of determining aconversion rate of a targeted message associated with a point ofinterest (POI), the POI being in a location, the location beingassociated with a local area network (LAN), the LAN including at leastone wireless access point broadcasting a wireless signal, the methodbeing executed at a server having a processor and a non-transient memorycommunicatively coupled to the processor, the non-transient memorystoring processor-executable instructions thereon for executing themethod.

The method includes determining during an in-use phase, by the server, aplurality of users that have been exposed to the targeted messageassociated with the POI within a first pre-determined time period, agiven user of the plurality of users using a respective wireless devicehaving a local area communication module and a wireless deviceidentifier associated with the local area communication module;receiving during the in-use phase, at the server based on the wirelessdevice identifiers of the wireless devices of the plurality of users, aplurality of sets of local area communication features of the local areacommunication modules of the wireless devices; and inputting, by theserver into a Machine Learning Algorithm (MLA) as an in-use input, theplurality of sets of local area communication features of the wirelessdevices, the MLA having been trained during a training phase based on atraining dataset associated with the POI, the training phase havingpreceded the in-use phase.

A given training object of the training dataset had been generated by:determining at least one geo-location of a training wireless device of atraining user, the at least one geo-location having a time associatedtherewith that occurred before the given time, the POI being in apre-determined vicinity of the at least geo-location; determining a setof training local area communication features that the training wirelessdevice had at the time associated with the at least one geo-location,the set of training local area communication features including anindication of the training wireless device having detected from the atleast one geo-location the wireless signal of the at least one wirelessaccess point of the LAN, the training wireless device having a trainingwireless device identifier, and a training local area communicationmodule, applying during the training phase a set of heuristics to a userprofile associated with the training user to generate a training labelindicative of whether the training user visited the POI at the timeassociated with the at least one geo-location, and thereby generatingduring the training phase the given training object having the set oftraining local area communication features and the training labelassociated therewith.

The method includes receiving, by the server from the MLA as an in-useoutput based on the plurality of sets of local area communicationfeatures of the wireless devices, a plurality of indications, a givenindication of the plurality of indications being associated with thegiven user of the plurality of users and indicating whether the givenuser visited the POI within a second pre-determined time period afterhaving been exposed to the targeted message; and determining during thein-use phase, by the server, based on the plurality of indications, aconversion rate parameter indicating the conversion rate of the targetedmessage, the conversion rate being a percentage of the plurality ofusers that have visited the POI within the second pre-determined timeperiod after having been exposed to the targeted message.

In some implementation of the method, the user profile comprises a userspecific portion and a device specific portion.

In some implementation of the method, the set of heuristics comprises afirst subset of heuristics applicable to the user specific portion, andthe first subset of heuristics being for executing a determination thatthe training user interacted with a resource associated with the POI,the resource including at least one of a telephone number associatedwith the POI, and a web site associated with the POI.

In some implementation of the method, the first subset of heuristics isbeing further for executing a determination that the training userinteracted with a web resource and the interaction with the web resourceincluded the training user executing a web search associated with thePOI.

In some implementation of the method, the web resource is a map serviceand the interaction was a search in the map service for the POI, thesearch having been executed by the training user.

In some implementation of the method, the method further comprisesdetermining, based on the training wireless device identifier: a set oftime-stamped geo-tracked locations of the training wireless device, theset of time-stamped geo-tracked locations being indicative of locationswhere the training wireless device was located over a pre-determinedperiod of time, and wherein the set of heuristics comprises a secondsubset of heuristics applicable to the device specific portion, and thesecond subset of heuristics being for executing a determination based onthe set of time-stamped geo-tracked locations that the training usermoved to within the pre-determined vicinity of the location of the POI.

In some implementation of the method, the second subset of heuristics isfurther for executing a determination that the training user was exposedto the targeted message associated with the POI before having moved towithin the pre-determined vicinity of the location of the POI.

In some implementation of the method, the targeted message is an onlinetargeted message.

In some implementation of the method, the targeted message is an offlinetargeted message.

In some implementation of the method, the second subset of heuristicsfurther for executing a determination that the training user moved towithin the pre-determined vicinity of the location of the POI within apre-determined time limit after having been exposed to the targetedmessage.

In some implementation of the method, the pre-determined time limit isless than five hours.

In some implementation of the method, the pre-determined vicinity isdefined by a radius of five hundred meters away from the location of thePOI.

In some implementation of the method, the pre-determined vicinity isdefined by a radius of one hundred and fifty meters away from thelocation of the POI.

In some implementation of the method, the second subset of heuristics isfurther being for executing a determination that the training wirelessdevice connected, via the training communication module, to the localarea network (LAN), and wherein the LAN is associated with the POI.

In some implementation of the method, the location is associated with aplurality of LANs, the plurality of LANs including the LAN, and whereinthe training local area communication features include at least one of:a Service Set ID (SSID) of each LAN of the plurality of LANs, a MACAddress of a wireless access point associated with each LAN of theplurality of LANs, and a signal strength of the wireless access pointassociated with each LAN of the plurality of LANs.

In some implementation of the method, the method further includesexecuting a feature engineering routine prior to the training phase, thefeature engineering routine determining a plurality of features that arepart of the set of training local area communication features, theplurality of features to be weighed more heavily by the MLA than therest of the set of training local area communication features during thetraining phase.

According to another aspect of the present technology, there is provideda method of determining a conversion rate of a targeted messageassociated with a POI, the method being executed at a server having aprocessor and a non-transient memory communicatively coupled to theprocessor, the non-transient memory storing processor-executableinstructions thereon for executing the method.

The method includes determining during an in-use phase, by the server, aplurality of users that have been exposed to a targeted messageassociated with the POI within a first pre-determined time period, eachuser of the plurality of users using a wireless device having a localarea communication module and a wireless device identifier associatedwith the local area communication module; receiving during the in-usephase, at the server based on the wireless device identifiers of thewireless devices of the plurality of users, a plurality of sets of localarea communication features of the local area communication modules ofthe wireless devices; inputting, by the server into the MLA as an in-useinput, the plurality of sets of local area communication features of thewireless devices; receiving, by the server from the MLA as an in-useoutput based on the plurality of sets of local area communicationfeatures of the wireless devices, a plurality of indications, a givenindication of the plurality of indications being associated with a givenuser of the plurality of users and indicating whether the given uservisited the POI within a second pre-determined time period after havingbeen exposed to the targeted message; and determining during the in-usephase, by the server, based on the plurality of indications, aconversion rate parameter indicating the conversion rate of the targetedmessage, the conversion rate being a percentage of the plurality ofusers that have visited the POI within the second pre-determined timeperiod after having been exposed to the targeted message.

According to another aspect of the present technology, there is provideda method of determining a Place Visit Lift (PVL) metric, the methodbeing executed at a server having a processor and a non-transient memorycommunicatively coupled to the processor, the non-transient memorystoring processor-executable instructions thereon for executing themethod.

The method includes determining, by the server, a visitors_siteparameter, the visitors_site parameter being equal to a number of usersthat both: a) were exposed to a targeted message directing users to apoint of interest (POI), and b) visited the POI after having beenexposed to the targeted message; determining, by the server, abypassers_site parameter, the bypassers_site parameter being equal to anumber of users that did not visit the POI after having been exposed tothe targeted message; determining, by the server, a visitors_non-siteparameter, the visitors_non-site parameter being equal to a number ofusers that were not exposed to the targeted message but visited the POI;determining, by the server, a bypassers_non-site parameter, thebypassers_non-site parameter being equal to a number of users that werenot exposed to the targeted message and did not visit the POI; anddetermining, by the server, the PVL metric according to a formula:

${PVL} = {{\frac{{visitors\_ site}/{bypassers\_ site}}{{visitors\_ non}\text{-}{{site}/{bypassers\_ non}}\text{-}{site}}*100\%} - {100{\%.}}}$

In some embodiments, at least one of the visitors_site, thebypassers_site, the visitors_non-site, and the bypassers_non-siteparameters being determined using the method of determining a fact of avisit as described herein above.

In some embodiments, the targeted message is an online targeted message.

In some embodiments, the targeted message is an offline targetedmessage.

The above examples are non-limiting.

DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a schematic diagram of a system suitable for implementingnon-limiting embodiments of the present technology.

FIG. 2 depicts a log containing geo-location coordinates, the log havingbeen generated or hosted by a wireless device of the system of FIG. 1 .

FIG. 3 depicts a digital content item database of the system of FIG. 1 .

FIG. 4 depicts a user profile database of the system of FIG. 1 .

FIG. 5 depicts a non-limiting example of a geographical area, POIs inthe geographical area, and users in the geographical area, each of theusers having at least one wireless device.

FIG. 6 depicts a logic flow diagram showing a non-limiting embodiment ofa method according to the present technology.

FIG. 7 depicts a logic flow diagram showing a non-limiting embodiment ofanother method according to the present technology.

FIG. 8 depicts a logic flow diagram showing a non-limiting embodiment ofyet another method according to the present technology.

DETAILED DESCRIPTION

Referring to FIG. 1 , there is shown a schematic diagram of a system100, the system 100 being suitable for implementing non-limitingembodiments of the present technology. It is to be expressly understoodthat the system 100 is depicted merely as an illustrative implementationof the present technology. Thus, the description thereof that follows isintended to be only a description of illustrative examples of thepresent technology. This description is not intended to define the scopeor set forth the bounds of the present technology.

In some cases, what are believed to be helpful examples of modificationsto the system 100 may also be set forth below. This is done merely as anaid to understanding, and, again, not to define the scope or set forththe bounds of the present technology. These modifications are not anexhaustive list, and as a person skilled in the art would understand,other modifications are likely possible. Further, where this has notbeen done (i.e. where no examples of modifications have been set forth),it should not be interpreted that no modifications are possible and/orthat what is described is the sole manner of implementing that elementof the present technology.

As a person skilled in the art would understand, this is likely not thecase. In addition, it is to be understood that the system 100 mayprovide in certain instances simple implementations of the presenttechnology, and that where such is the case they have been presented inthis manner as an aid to understanding. As persons skilled in the artwould understand, various implementations of the present technology maybe of a greater complexity.

The examples and conditional language recited herein are principallyintended to aid the reader in understanding the principles of thepresent technology and not to limit its scope to such specificallyrecited examples and conditions. It will be appreciated that thoseskilled in the art may devise various arrangements which, although notexplicitly described or shown herein, nonetheless embody the principlesof the present technology and are included within its spirit and scope.Furthermore, as an aid to understanding, the following description maydescribe relatively simplified implementations of the presenttechnology. As persons skilled in the art would understand, variousimplementations of the present technology may be of greater complexity.

Moreover, all statements herein reciting principles, aspects, andimplementations of the present technology, as well as specific examplesthereof, are intended to encompass both structural and functionalequivalents thereof, whether they are currently known or developed inthe future. Thus, for example, it will be appreciated by those skilledin the art that any block diagrams herein represent conceptual views ofillustrative circuitry embodying the principles of the presenttechnology. Similarly, it will be appreciated that any flowcharts, flowdiagrams, state transition diagrams, pseudo-code, and the like representvarious processes which may be substantially represented incomputer-readable media and so executed by a computer or processor,whether or not such computer or processor is explicitly shown.

The functions of the various elements shown in the figures, includingany functional block labelled as a “processor” may be provided throughthe use of dedicated hardware as well as hardware capable of executingsoftware in association with appropriate software. When provided by aprocessor, the functions may be provided by a single dedicatedprocessor, by a single shared processor, or by a plurality of individualprocessors, some of which may be shared.

In some embodiments of the present technology, a processor may be ageneral purpose processor, such as a central processing unit (CPU) or aprocessor dedicated to a specific purpose, such as a graphics processingunit (GPU). Moreover, explicit use of the term “processor” or“controller” should not be construed to refer exclusively to hardwarecapable of executing software, and may implicitly include, withoutlimitation, digital signal processor (DSP) hardware, network processor,application specific integrated circuit (ASIC), field programmable gatearray (FPGA), read-only memory (ROM) for storing software, random accessmemory (RAM), and non-volatile storage. Other hardware, conventionaland/or custom, may also be included.

With these fundamentals in place, we will now consider some non-limitingexamples to illustrate various implementations of aspects of the presenttechnology.

Referring to FIG. 1 , the system 100 has access to a plurality ofwireless devices 101. Some of the wireless devices 101 are shown in FIG.1 . The wireless devices 101 are similar to each other at least insofaras being suitable for use with the present technology. Therefore, only arepresentative one of the wireless devices 101, namely wireless device102, is described herein in detail. The wireless device 102 is typicallya portable wireless device that is associated with a user (not depicted)and, as such, can sometimes be referred to as a “client device”. Itshould be noted that the fact that the wireless device 102 is associatedwith the user does not mean to suggest or imply any mode ofoperation—such as a need to log in, a need to be registered or the like.

In the context of the present specification, unless provided expresslyotherwise, a “wireless device” is any portable wireless device having atleast one kind of geo-location means for determining its owngeo-location and at least having Wi-Fi connectivity capability toconnect to local area networks (LANs) and/or other devices. Somenon-limiting examples of wireless devices include Wi-Fi enabled andGlobal Positioning System (GPS) enabled smartphones and tablets that aretypically carried around by their users wherever they go. It should benoted that a device acting as a wireless device in the present contextis not precluded from acting as a server to other wireless devices. Theuse of the expression “a wireless device” does not preclude multipleclient devices being used in receiving/sending, carrying out or causingto be carried out any task or request, or the consequences of any taskor request, or steps of any method described herein.

The wireless device 102 comprises a permanent storage 104 storingprocessor-executable instructions therein, a processor 106communicatively coupled to the permanent storage 104, and a local areacommunication module 109 communicatively coupled to the processor 106.The permanent storage 104 may encompass one or more storage media andprovides a place to store computer-executable instructions executable bya processor 106. By way of an example, the permanent storage 104 may beimplemented as a computer-readable storage medium including Read-OnlyMemory (ROM), hard disk drives (HDDs), solid-state drives (SSDs), andflash-memory cards. The local area communication module 109 in thepresent embodiment includes a Wi-Fi module capable of connecting to alocal area network (LAN) that may be shared via a Wi-Fi access pointsuch as a wireless router. In some embodiments, the local areacommunication module 109 also includes a Bluetooth™ module for Bluetoothconnectivity.

The wireless device 102 further comprises hardware and/or softwareand/or firmware (or a combination thereof) to execute a plurality ofapplications 107, including a navigation application 108 stored at leastin part in the permanent storage 104. One purpose of the navigationapplication 108 is to enable the user to navigate from one location toanother location. The manner in which the navigation application 108 isimplemented is known in the art and will not be described herein. Forexample, the navigation application 108 may be one of Yandex.Maps™,Yandex.Navigator™, or other commercial or proprietary map application.

The wireless device 102 also has access to a GPS receiver configured toreceive GPS satellite signals and determine the geo-location of thewireless device 102, or other means of determining the geo-location ofthe wireless device 102 at given times and/or time intervals. In thepresent embodiment, the wireless device 102 includes the GPS receiveron-board. In at least some embodiments, the wireless device 102 does notneed to be connected to the Internet, nor be executing the navigationapplication 108, to receive the GPS satellite signals, and as such thewireless device 102 may be configured to track the movement of user inthe form of geo-locations that may be recorded in terms of latitude andlongitude irrespective of the availability of the Internet connection.In the present embodiment, the wireless device 102 determines its GPScoordinates at a given frequency which depends on each particularembodiment of the wireless device 102 and its operating system forexample, and records them in a log 110.

In some embodiments, the wireless device 102 stores the log 110on-board, in the permanent storage 104. In some embodiments, the log 110is stored remote to the wireless device 102, such as at a remote server.In some embodiments, the wireless device 102 makes its geo-locationavailable to one or more remote servers. In some embodiments, thewireless device 102 makes its geo-location available to one or moreremote servers in or near real-time.

In some non-limiting embodiments, the log 110 stores the geo-locationsof the wireless device 102 as timestamped geo-location coordinates ofthe wireless device 102 that have been collected or provided by thewireless device 102 over time. In some embodiments, the wireless device102 collects and in some cases hosts the log 110 and/or its timestampedGPS coordinates, such as via a combination of the applications 107and/or via the operating system of the wireless device 102 without anyparticular application stored on the wireless device 102 being opened bya user thereof.

With reference to FIG. 2 , a non-limiting embodiment of the log 110 isillustrated. The manner and format in which the log 110 is populated andstored is not limited. As an example, the wireless device 102 may beconfigured to receive the GPS satellite signals at predetermined timeintervals, and convert the GPS satellite signals into timestampedgeo-location coordinates. In some embodiments, the wireless device 102comprises and/or uses cell-tower triangulation or other geo-locationdetermining means to obtain its geo-locations, either instead of or inaddition to using a GPS system.

In the example shown in FIG. 2 , the log 110 comprises a firstgeo-location coordinate 202 which corresponds to, for the sake of anon-limiting example, a MacDonald's™ 500 in a given geographical area136. The first geo-location coordinate 202 is associated with a firsttimestamp 204. The first timestamp 204 corresponds to the time at whichthe wireless device 102 was at the first geo-location coordinate 202.The log 110 further comprises additional geo-location coordinates andassociated time stamps (not separately numbered). For example, based onthe time stamps and geo-location coordinates within the log 110, itcould be understood that the wireless device 102 has moved 0.5 secondsnorth and 0.4 seconds west within 6 minutes. Only four geo-locations areillustrated in the log 110, to maintain clarity. The log 110 maycomprise more or fewer geo-locations/coordinates.

In some embodiments, the wireless device 102 is configured to assign auser device ID 206 to the log 110. For example, the user device ID 206may be a MAC address of the wireless device 102 or a component thereof,and/or may be an indication of a user name (such as an email address)associated with the user if the wireless device 102 requires the usersigning-in, and so on. In another example, the user device ID 206 maycorrespond to a proprietary ID number assigned by, for example, thenavigation application 108 and/or other service application(s) 112(described below). The user device ID 206 is thus one example of awireless device identifier 206 of the wireless device 102.

Referring back to FIG. 1 , the wireless device 102 comprises hardwareand/or software and/or firmware (or a combination thereof) to executeone or more service applications 112. In some embodiments, the one ormore service applications 112 comprise at least one service application(not numbered) that is operated by the same entity that has provided theafore-described navigation application 108. For example, if thenavigation application 108 is Yandex.Navigator, the one or more serviceapplications 112 may include a web browser application Yandex.Browser™,a news application Yandex.News™, a market application Yandex.Market™,and the like. Needless to say, the one or more service applications 112may also include service applications that are not operated by the sameentity that has provided the afore-mentioned navigation application 108,and may comprise for example, social media applications such asVkontakte™, and music streaming application such as Spotify™.

In some embodiments the activities of the user executed via the wirelessdevice 102 are collected by one or more servers, and are used to build aprofile of the user, or simply a user profile, associated with the userand/or the user's wireless device(s) 102. In some embodiments, the userprofile comprises a user specific portion comprising information relatedto the user and a device specific portion comprising information relatedto the user's wireless device(s) 102. In some embodiments, the one ormore service applications 112 that are operated by the same entity asthe navigation application 108 are configured to report and/or storeuser activities and/or geo-locations with an accompanying indication ofthe unique user ID 206 or other means for allowing subsequentidentification of the user's activity/geo-location history with theuser's wireless device(s) 102 via which the user performed the actionsconstituting the user's activity history.

The wireless device 102 comprises a communication interface (notdepicted) for enabling two-way communication with a communicationnetwork 114 via a communication link 116. In some non-limitingembodiments of the present technology, the communication network 114 canbe implemented as the Internet. In other embodiments of the presenttechnology, the communication network 114 can be implementeddifferently, such as any wide-area communication network, local areacommunications network, a private communications network and the like.In some embodiments, the communication interface includes and/oroperates with the local area communication module 109 of the wirelessdevice 102.

How the communication link 116 is implemented is not particularlylimited and depends on how the wireless device 102 is implemented.Merely as an example and not as a limitation, in those embodiments ofthe present technology where the wireless device 102 is implemented as awireless device (such as a smart phone), the communication link 116 canbe implemented as a wireless communication link (such as, but notlimited to, a 3G communications network link, a 4G communicationsnetwork link, a Wireless Fidelity, or WiFi®, for short, Bluetooth®, orthe like) or wired (such as an Ethernet based connection).

It should be expressly understood that implementations for the wirelessdevice 102, the communication link 116 and the communication network 114are provided for illustration purposes only. As such, those skilled inthe art will easily appreciate other specific implementational detailsfor the wireless device 102, the communication link 116, and thecommunication network 114. As such, by no means the examples providedhereinabove are meant to limit the scope of the present technology.

Still referring to FIG. 1 , the system 100 further includes a server 118coupled to the communication network 114. The server 118 can beimplemented as a computer server. In an example of an embodiment of thepresent technology, the server 118 can be implemented as a Dell™PowerEdge™ Server running the Microsoft™ Windows Server™ operatingsystem. Needless to say, the server 118 can be implemented in any othersuitable hardware and/or software and/or firmware or a combinationthereof. In the depicted non-limiting embodiment of the presenttechnology, the server 118 is a single server. In alternativenon-limiting embodiments of the present technology, the functionality ofthe server 118 may be distributed and may be implemented via multipleservers.

The server 118 comprises a communication interface 119 structured andconfigured to communicate with various entities (such as the wirelessdevices 101, and other devices potentially coupled to the communicationnetwork 114) via the communication network 114. The server 118 comprisesa non-transient memory 120 which comprises one or more storage media andgenerally provides a place to store computer-executable programinstructions executable by a processor 122. By way of example, thememory 120 may be implemented as a tangible computer-readable storagemedium including Read-Only Memory (ROM) and/or Random-Access Memory(RAM). The memory 120 may also include one or more storage devices inthe form of, by way of example, hard disk drives (HDDs), solid-statedrives (SSDs), and flash-memory cards.

In some embodiments, the server 118 can be operated by the same entitythat has provided the afore-described navigation application 108. Forexample, if the navigation application 108 is a Yandex.Navigator, theserver 118 can be operated by Yandex LLC of Lev Tolstoy Street, No. 16,Moscow, 119021, Russia. In alternative embodiments, the server 118 canbe operated by an entity different from the one that has provided theaforementioned navigation application 108. It is noted that while theserver 118 is described herein below in some non-limiting embodiments asbeing configured with a particular set of modules providing variouscombinations of functions, in alternative non-limiting embodiments theserver 118 could be configured with a different set of modules and/or inat least partially a distributed manner, so long as the functionalitydescribed in this document is provided.

In accordance with some non-limiting embodiments of the presenttechnology, the server 118 is configured to execute a digital contentitem selection application 124 (the “selection application 124”). Themanner in which the selection application 124 is implemented isdescribed in detail below.

Digital Content Item Database

To that end, the server 118 is communicatively coupled to a digitalcontent item database 126. In alternative embodiments, the digitalcontent item database 126 may be communicatively coupled to the server118 via the communication network 114. Although the digital content itemdatabase 126 is illustrated schematically herein as a single entity, itis contemplated that the digital content item database 126 may beconfigured in a distributed manner.

The digital content item database 126 is populated with a plurality ofdigital content items (not separately numbered). The nature of each ofthe plurality of digital content item is not particularly limited.Broadly speaking, a digital content item may correspond to a targetedmessage relating to one or more points of interest (POI), and comprisingone or more sentences, images, videos, etc.

With reference to FIG. 3 , a non-limiting embodiment of the digitalcontent item database 126 populated with the plurality of digitalcontent item is illustrated.

The manner in which the digital content item database 126 is populatedis not limited. Just as an example the digital content item database 126may receive the digital content items from one or more contentproviders.

In at least some non-limiting embodiments of the present technology, thedigital content item database 126 can store the plurality of digitalcontent items clustered into one or more topics. As such, the digitalcontent item database 126 can be configured to execute a topicclustering routine (not depicted). The manner in which the plurality ofdigital content items are clustered into one or more topics or events isnot limited, and may for example, be done using conventional clusteringtechniques, such as topic modelling or key word based approaches.

The plurality of digital content items is clustered into one or moretopic clusters 302. For example, the digital content item database 126stores a first topic cluster 304, a second topic cluster 306, a thirdtopic cluster (not numbered) and a fourth topic cluster (not numbered).The first topic cluster 304 includes a first digital content item 308and a second digital content item 312, and the second topic cluster 306includes a third digital content item 310.

In some non-limiting embodiments of the present technology, the digitalcontent item database 126 comprises additional information in respect toeach of the plurality of digital content items, such as the duration ofthe digital content item, a minimum bidding price, target parametersselected by a source of the digital content item, indication of thedigital content item being static or dynamic, and the like.

User Profile Database

With reference to FIG. 1 , the server 118 is further in communicationwith a user profile database 128 that, inter alia, stores the userprofiles 129 (described above) of the users of the wireless devices 101.In alternative non-limiting embodiments of the present technology, theuser profile database 128 may be communicatively coupled to the server118 via the communication network 114. Although the user profiledatabase 128 is illustrated schematically herein as a single entity, itis contemplated that the user profile database 128 may be configured ina distributed or any other manner.

The user profile database 128 is a repository of the user profiles 129,each one of which is associated with one or more of the wireless devices101. How the one or more user profiles 129 are stored is not limited,and may for example be a set of vectors representing the interests of agiven user of one or more of the wireless devices 101.

With reference to FIG. 4 , a schematic illustration of the aggregationof different user profiles associated with the user of the wirelessdevice 102, the wireless device 102 being an example one of the wirelessdevices 101, is depicted.

A first profile 402 is received from a first service server 404. Forexample, the first service server 404 may be associated with a firstservice application 401 that corresponds to Yandex.Browser, which isoperated by the same entity providing the aforementioned navigationapplication 108 on the wireless device 102. The first profile 402 may begenerated by the first service server 404 based on for example thebrowsing logs 403 associated with the wireless device 102.

The first profile 402 is associated with a first unique ID 406, which isa proprietary user ID, associated with the wireless device 102. Moreparticularly, and recalling that the first service application 401 isoperated by the same entity providing the aforementioned navigationapplication 108 on the wireless device 102, the first unique ID 406 ofthe user's first profile 402 corresponds to the user device ID 206 ofthe wireless device 102.

A second profile 408 for the user of the wireless device 102 is receivedfrom a second service server 410. For example, the second service server410 may be associated with a second service application 409 on thewireless device 102 that corresponds to Yandex.Market, which is operatedby the same entity providing the aforementioned navigation application108. The second profile 408 may be generated by the second serviceserver 410 based for example on search logs 411 associated with searchesexecuted by the user of the wireless device 102 via Yandex.Market on thewireless device 102.

The second profile 408 is also associated with a second unique ID 412.Given that Yandex.Market requires an email address to be provided by theuser to access the digital service, the second unique ID 412 comprisesboth the email address of the user as well as the proprietary user ID,which corresponds to the user device ID 206.

In some non-limiting embodiments, the user profile database 128 isconfigured to execute a profile aggregation routine (not depicted). Theprofile aggregation routine is configured to determine if the firstprofile 402 and the second profile 408 correspond to the same user. Forexample, the profile aggregation routine may be configured to determineif the first unique ID 406 corresponds, at least partially, to thesecond unique ID 412.

If it is determined that the first unique ID 406 corresponds at leastpartially to the second unique ID 412, the profile aggregation routineis configured to aggregate the first profile 402 and the second profile408 to generate an aggregated profile 414.

As a result of the execution of the profile aggregation routine, theuser profile database 128 stores the aggregated profile 414 togetherwith a third unique ID 416 (which corresponds to the user device ID 206and the email address).

On the other hand, if the profile aggregation routine determines thatthe first unique ID 406 does not correspond even partially to the secondunique ID 412, the first profile 402 and the second profile 408 areconsidered to be associated with different users. Consequently the userprofile database 128 stores the first profile 402 (and the first uniqueID 406) and the second profile 408 (and the second unique ID 412)separately.

Needless to say, although only two user profiles (the first profile 402and the second profile 408) are illustrated to generate the aggregatedprofile 414, it should be understood that the aggregated profile 414 maybe generated based on more than two user profiles for a given wirelessdevice 101.

Moreover, although the aggregated profile 414 has been generated basedsolely on service applications that are operated by the same entity, itis not limited as such. Given that the second user profile 412 includesthe email address associated with the user, it is possible to furtheraggregate the user's profile with a third user profile (not shown) thatis received from a different entity, provided that the third userprofile is also associated with a third unique ID that corresponds tothe same email address.

In summary, the user profile database 128 stores a plurality of userprofiles 129, with each of the user profiles 129 being associated withat least one of the wireless devices 101. In some non-limitingembodiments of the present technology, the user profiles 129 includeadditional information about the users to which they belong. Onenon-limiting example of such additional information is the users'genders. Another non-limiting example are the users' purchase histories.

Metrics Module

Referring back to FIG. 1 , in some non-limiting embodiments, the server118 stores in its memory 120 and executes a metrics module 139. Themetrics module 139 retrieves timestamped geo-locations of the wirelessdevices 101 to which the server 118 has access, over a period of time.In some embodiments, the metrics module 139 retrieves the timestampedgeo-location data continuously as the timestamped geo-location databecomes available on each given wireless device 101. The metrics module139 stores the timestamped geo-location data of each given wirelessdevice 101 with the associated unique ID of the given wireless device101, in a user geo-location database 141. In some embodiments, themetrics module 139 further configures the user geo-location database 141by associating therein the geo-location data of each given wirelessdevice 101 with the corresponding user profile 402 and/or the uniqueuser ID 206 or other corresponding unique identifier.

Now also referring to FIG. 5 , the metrics module 139 further retrievesfrom each given wireless device 101 information about LANs 135 that aredetected by each given wireless device 101 at various geo-locations, atleast at some given data collection frequency. The data collectionfrequency may be different for different wireless devices 101, and maybe a function of the particular embodiment of each given wireless device101 and/or each given wireless device's 101 operating system and/orconfiguration. As a non-limiting example, a given wireless device 101may detect what LANs 135 are available at a frequency of ⅓ hertz, oronce every three seconds.

As schematically shown in FIG. 5 , a given LAN 135 may include awireless access point 137 via which the given LAN 135 may be connectedto by one or more wireless devices 101. As a non-limiting example, thewireless access point 137 may be a router. A given wireless device 101may detect a wireless signal emanating from the wireless access point137 of the LAN 135. Based on the wireless signal, each given wirelessdevice 101 may determine all of: 1) a media access control address (MACAddress) of the wireless access point 137, 2) a Service Set ID (SSID) ofthe LAN 135, and 3) a strength of the wireless signal. In someembodiments, each given wireless device 101 may further deriveadditional metrics from the detected wireless signal which may beindicative of at least some characteristics of the wireless access point137.

At any given geo-location, a given wireless device 101 may detect aplurality of wireless signals emanating from different wireless accesspoints 137 that the given wireless device 101 may be in range of. Thegiven wireless device 101 may derive the MAC Address, the SSID, and thesignal strength for each of the detected wireless signals. As anon-limiting example, and still referring to FIG. 5 , at a given pointin time a given user 103 of a given wireless device 102 may be visitingthe MacDonald's 500 in the geographical area 136. While the user 103 isinside the establishment, the user's 103 wireless device 102 may detectthe following set of Wi-Fi's: 1) a LAN 135 that belongs to theMacDonald's 500 itself, 2) a LAN 135 in a residence 501 that is locatedin proximity to the MacDonald's 500; and 3) a LAN 135 shared via a Wi-Fihotspot on a portable wireless device 502 of another user 504 thathappened to be in the MacDonald's 500 at the same time as the user 103.

As another non-limiting example, after visiting the MacDonald's 500, theuser 103 may decide to walk over, as shown with arrow 506, to aPetro-Canada™ gas station 508 that happens to be nearby. At the latercorresponding time when the user 103 is at the gas station 508, theuser's 103 wireless device 102 may detect a different set of Wi-Fi's: 1)a LAN 135 that belongs to the gas station 508, and 2) the LAN 135 thatbelongs to the MacDonald's 500 because the user is still in rangethereof.

As shown in FIG. 5 , when the user 103 is at the gas station 508, theuser's 103 wireless device 102 is out of range of all of the residentialLANs in the geographical area 136 and out of range of the Wi-Fi hotspotof the other user 504 because the other user's device 502 emits a weakerwireless signal than the wireless access point 137 belonging to theMacDonald's 500.

It will be appreciated that as the user 103 was walking from theMacDonald's 500 to the gas station 508, the user's 103 wireless device102 may have detected additional, different, sets of LANs at the timefrequency associated with the wireless device 102.

The MAC Address(es), the SSID(s) and the signal strength(s) detected byeach given wireless device 101 at each given point in time, as well asthe associated time of detection and any additional metric(s) that maybe determined by the given wireless device 101, may be collectivelyreferred to as: a timestamped LAN snapshot 123 (FIG. 1 ). The metricsmodule 139 may retrieve from each of the wireless devices 101 theirtimestamped LAN snapshots 123, for example at the time frequenciesassociated with each given wireless device 101, and store the LANsnapshots 123 in a LAN snapshot database 121 in a suitable datastructure.

In some non-limiting embodiments, the metrics module 139 may retrievethe LAN snapshots 123 from all of the wireless devices 101 to which ithas access in batches, at pre-determined time intervals for example. Theserver 118 may thus use the collected LAN snapshots 123 from all of thewireless devices 101 to generate the LAN snapshot database 121.

In some non-limiting embodiments, the metrics module 139 may beconfigured to filter at least some of the LANs out of thecollected/stored LAN snapshots 123. For example, in some non-limitingembodiments, the server 118 and/or the metrics module 139 may beconfigured to determine/detect, for each given wireless device 101,which of the LANs by the given wireless device 101 at any point in time,or at least in a given number of sequential LAN snapshots 123, arestatic LANs and which are mobile/dynamic LANs.

The particular methods/algorithms used to discern and filter outmobile/dynamic LANs from static LANs are not limited. Any suitablemethods/algorithms may be used for this purpose. The resulting filteredsequential LAN snapshots 123 are representative solely of static LANs.In at least some non-limiting embodiments, the static LANs snapshots 123help establish more reliable fingerprints of POIs 134, as describedbelow in more detail.

In some non-limiting embodiments, the metrics module 139 may beconfigured to delete from the LAN snapshot database 121, or simplyinitially not store in the LAN snapshot database 121, the MACAddress(es), the SSID(s), the signal strength(s), and any additionalinformation related to any mobile/dynamic LANs determined to have beenpresent in any of the LAN snapshot(s) 123.

Accordingly, in such non-limiting embodiments, the LAN snapshot database121 may contain the information for only the static LANs by the wirelessdevices 101. One non-limiting example of a static LAN is a LAN 135 theSSID and the geo-location of which do not change at least over apre-determined time period, such as a month. A non-limiting example of amobile/dynamic LAN is a LAN the geo-location of which changes over apre-determined time period, such as a day. A particular non-limitingexample of a mobile/dynamic LAN is the LAN shared via the Wi-Fi hotspoton the portable wireless device 502 of user 504 in FIG. 5 .

It is contemplated that additional and/or different definitions for thestatic LANs and/or the mobile/dynamic LANs could be used instead of orin addition to the abovementioned non-limiting examples thereof.

POI Databases

With reference to FIG. 1 , the server 118 may be further incommunication with a remote Point of Interest (POI) profile database 130and a POI fingerprint database 131. In some non-limiting embodiments ofthe present technology, the remote POI profile database 130 may becommunicatively coupled to the server 118 via a local connection orother direct link. In some non-limiting embodiments of the presenttechnology, the POI fingerprint database 131 may be a remote databasecommunicatively coupled to the server 118 via the communication network114. Although the POI profile database 130 and the POI fingerprintdatabase 131 are illustrated schematically herein as single entities, itis contemplated that one or both of the databases 130 and 131 may beimplemented in a distributed manner.

In some non-limiting embodiments, the POI profile database 130 may storea plurality of POI profiles 132 of POIs 134 located in a givengeographical area 136. A non-limiting example of a geographical area 136and POIs 134 located therein are shown schematically in FIG. 5 . Thegeographical area 136 may be for example, a given region in a country, agiven city, a particular neighborhood of a city, a particular street,and so on. The POIs 134 may be, for example, businesses, services,landmarks, and so on. Some particular examples of POIs 134, as shown inFIG. 5 , may include a MacDonald's 500 and a Petro-Canada gas station508. The POI profile 132 of a given POI 134 may include, for example,hours of operation of the given POI 134, a category of the given POI134, a geo-location of the given POI 134, a rating of the POI 134, andso on.

POI Fingerprint Module

Referring to FIG. 1 , in some non-limiting embodiments, the server 118may further store in its memory 120 and execute a POI fingerprint module140. In summary, and as described in detail next, the POI fingerprintmodule 140 generates a POI fingerprint database 131 for the POIs 134 inthe geographical area 136, based on at least a first subset of the LANsnapshots 123 captured/retrieved by the metrics module 139 over at leasta first period of time and stored in the LAN snapshot database 121.

Once generated, the POI fingerprint database 131 stores at least one POIfingerprint 157 for each POI 134 in the geographic area 136. A POIfingerprint 157 of a given POI 134 is indicative of a typical set ofLAN(s) 135, if any, and their associated characteristics, as they aretypically detected by a given wireless device 101 of a user while theuser is visiting the given POI 134. An example process implemented bythe POI fingerprint module 140 is described next, in detail.

In some non-limiting embodiments, the POI fingerprint module 140builds/defines the POI fingerprint database 131 to contain therein, foreach user of each given one of the wireless devices 101, a datasetindicative of the POIs 134 that the user visited over a given timeframe,and the times at which the user made each of the visits. As described inmore detail below, the POI fingerprint module 140 will then determineone or more LAN snapshots 123 that correspond to each determined visitto each given POI 134. The POI fingerprint module 140 will therebydetermine a set of LAN snapshots 123 that occurred at different timesand from different wireless devices 101 at each given one of the POIs134. Based on each such set of LAN snapshots 123 for each given POI 134,the POI fingerprint module 140 will determine a fingerprintrepresentative of the POI 134. This process is described in detail next.

The POI fingerprint module 140 accesses the user profile database 128 toobtain the user IDs 206, the user profiles 129, and the deviceidentifiers of the corresponding wireless devices 101. Based on at leastsome of this information, the POI fingerprint module 140 may thenidentify the logs 110 of the corresponding wireless devices 101 andretrieve therefrom the geo-location data associated with the wirelessdevices 101 for at least a pre-defined timeframe, such as a given month,in the geographical area 136. The POI fingerprint module 140 may thenaccess the POI profile database 130 and retrieve therefrom the profilesof the POIs 134 in the geographical area 136.

The POI fingerprint module 140 may then apply a set of heuristics to theabovementioned retrieved/determined data and thereby determine, for eachgiven user-and-user-device combination, the particular ones of thePOI(s) 134, if any, that the given user visited over the pre-definedtimeframe and the time of each given visit.

As a non-limiting example, and referring to FIG. 5 , the POI fingerprintmodule 140 may determine that the user 103 having the wireless device102 visited the MacDonald's 500 from 12:00 pm to 12:10 pm on Nov. 29,2018, from 1:00 pm to 2:00 pm on Nov. 31, 2018, and from 6:25 pm to 6:45pm on Dec. 1, 2018. The POI fingerprint module 140 may also determinethat another user 504 having a wireless device 502, visited theMacDonald's) 500 from 9:00 am to 10:00 am on Nov. 29, 2018, and from1:30 pm to 2:30 pm on Nov. 30, 2018. The POI fingerprint module 140 mayalso determine a plurality of other visits by other users to the sameestablishment.

The POI fingerprint module 140 may in the same way determine a pluralityof different visits by a plurality of different users to different POIs134 in the geographical area 136. Suffice it to say that the POIfingerprint module 140 performs similar steps for the visits by theother users. To maintain simplicity, the present example continues witha discussion only of the user 103 and the associated steps of the POIfingerprint module 140.

Thus, using the user 103 as an example of one of the many users withregard to which the POI fingerprint module 140 determines facts ofvisits, the POI fingerprint module 140 may determine a plurality oftimestamped geo-locations in the log 110 of the wireless device 102 ofthe user 103 that are representative of each of the three visits to theMacDonald's 500. For example, for the first visit, the POI fingerprintmodule 140 may determine two hundred timestamped geo-locations reportedby the wireless device 102 of the user 103 that are associated with thefirst visit, if the wireless device 102 was at the time reporting itsgeo-location at a frequency of ⅓ hertz (Hz).

Set of Heuristics

To enable the abovementioned determinations of facts and times of visitsby the different users to the different POIs 134, the set of heuristicscomprises a first subset of heuristics applicable to the user specificportion of each given user profile 129. In some embodiments, the firstsubset of heuristics is configured for executing a determination thatthe relevant user interacted with a resource associated with a given POI134.

The resource associated with a given POI 134, in some non-limitingembodiments, may include at least one of a telephone line associatedwith the POI 134, and a website associated with the POI 134. The websiteis one example of a web resource associated with the POI 134. Otherexamples of web resources are a map service. For the MacDonald's 500visit example above, the telephone line may be a telephone line of theparticular location of the MacDonald's 500, the website may be theMacDonald's general website, and the map service may be the navigationapplication 108 of the user's 103 wireless device 102.

In some non-limiting embodiments, the set of heuristics is furtherconfigured for executing determinations of when a given user of a givenone of the wireless devices 101 interacted with a web resource of agiven POI 134, and when the interaction included the user executing aweb search associated with the POI 134. In the example of theMacDonald's 500, the first subset of heuristics may determine that theuser 103 executed a search for the location of the MacDonald's 500 viathe navigation application 108 of the wireless device 102 and that theuser 103 subsequently arrived in a pre-defined vicinity of the locationof the MacDonald's 500.

In the example of the user's 103 visiting the gas station 508, the firstsubset of heuristics may determine that the user 103 searched for aproduct, such as an energy drink, via a search engine executing on thewireless device 102, that the search engine returned an indication thatthe product is available at the gas station 508, and that the user's 103geo-track in the associated timeframe was directed toward the gasstation 508. It is contemplated that the first subset of heuristics mayrender its determinations of visits further based on other combinationsof satisfied conditions.

In some non-limiting embodiments, the set of heuristics may furthercomprise a second subset of heuristics applicable to the device specificportion of the user profile 129 associated with each given wirelessdevice 101 of each given user. The second subset of heuristics may beapplicable to a set of timestamped geo-tracked locations of each givenwireless device 101 that are indicative of locations where the givenwireless device 101 was located over a pre-determined period of time.More particularly, the POI fingerprint module 140 may apply the secondsubset of heuristics to the set of timestamped geo-tracked locations ofa given wireless device 101 and thereby execute a determination that theuser of the given wireless device 101 moved to within a pre-determinedvicinity of the location of the POI 134.

In the case of the user's 103 visits to the MacDonald's 500, the POIfingerprint module 140 may thereby execute a determination that the user103 of the wireless device 102 moved to within, and remained within, thepre-determined vicinity, let's say 150 meters, of the location of theMacDonald's 500 during the times associated with each visit. That is,from 12:00 pm to 12:10 pm on Nov. 29, 2018, from 1:00 pm to 2:00 pm onNov. 31, 2018, and from 6:25 pm to 6:45 pm on Dec. 1, 2018.

Further, in some non-limiting embodiments, the second subset ofheuristics is configured for executing a determination that a given userwas exposed to a targeted message associated with a given one of thePOIs 134 before having moved to within the pre-determined vicinity ofthe location of a given one of the POI 134. In some non-limitingembodiments, the targeted message may be an online message. In theexample of the MacDonald's 500, an online targeted message may be anindication that a deal is available for one of the products offered atthe MacDonald's 500, the indication of the deal having been delivered tothe wireless device 102 of the user 103.

In some non-limiting embodiments, the targeted message may be an offlinemessage. In the example of the MacDonald's 500, an offline message maybe an indication that the deal is available for one of the productsoffered at the MacDonald's 500, the indication of the deal having beendelivered to the attention of the user 103 via a billboard 150 (FIG. 5 )located in the geographic area 136.

In some non-limiting embodiments, the second subset of heuristics isfurther configured for executing a determination that a given user movedto within the pre-determined vicinity of the location of a given POI 134within a pre-determined time limit after having been exposed to atargeted message associated with the given POI 134.

As an example, in some non-limiting embodiments, the pre-determined timelimit programmed into the second subset of heuristics may be five hours.In some non-limiting embodiments, the pre-determined vicinity may bedefined by a radius of five hundred meters away from the location of agiven POI 134. In some non-limiting embodiments, the pre-determinedvicinity may be defined by a radius of one hundred and fifty meters awayfrom the location of a given POI 134.

In the example of the user's 103 visiting the gas station 508, thesecond subset of heuristics may determine that an advertisement(targeted message) for an energy drink available at the gas station 508was delivered to the user's wireless device 102. The the second subsetof heuristics may further determine that the user 103, within 30 minutesof being exposed to the advertisement, moved to within a 150 meters ofthe gas station 508. Accordingly, based on this combination ofdeterminations, the second subset of heuristics may render adetermination that the user 103 visited the gas station 508.

In some such non-limiting embodiments, the facts of exposure andassociated times of exposure of identified users to a given one or moreidentified targeted messages, including online targeted messages andoffline targeted messages, may be determined by the server 118 using thetechnology described in a co-owned U.S. patent application Ser. No.16/739,668, filed on Jan. 10, 2020 by the same applicant, entitled“METHOD AND SYSTEM FOR PROVIDING A RECOMMENDED DIGITAL CONTENT ITEM”,and incorporated herein by reference in its entirety. It is contemplatedthat other methods of determining exposure times of users to targetedmessages could also be used.

In some non-limiting embodiments, the second subset of heuristics isfurther configured for executing a determination that a given wirelessdevice 101 connected, via the local area communication module 109thereof, to a LAN 135 associated with the location of a given POI 134and/or with the given POI 134 itself. In the example of the gas station508 being visited by the user 103, the second subset of heuristics maydetermine that after arriving within the 50 meters of the gas station508, the user 103 connected to the LAN 135 of the gas station 508 viaher wireless device 102.

In summary, the set of heuristics enables the POI fingerprint module 140to determine facts and times of visits by identified users to identifiedones of the POIs 134. In at least some non-limiting embodiments, the POIfingerprint module 140 stores indications of the determined visitsand/or times in the POI fingerprint database 131.

Once a first set of facts of visits by different users to each of thePOIs 134 in the geographical area 136 are determined, the POIfingerprint module 140 retrieves the LAN snapshots 123 that correspondto each of the first set of facts and times of visits.

More particularly, in some non-limiting embodiments, over apre-determined timeframe, for each given POI 134 and for each determinedpair of: i) fact of visit, and ii) time of visit to that given POI 134,by a given wireless device 101 during the pre-determined timeframe, thePOI fingerprint module 140 retrieves from the LAN snapshot database 121the timestamped LAN snapshot 123 of the given wireless device 101. Inother words, the POI fingerprint module 140 retrieves a dataset that isindicative of the LANs 135 and their corresponding metrics that weredetected/determined by users' wireless devices 101 at each of the timesof the visits by the users to each of the respective ones of the POIs134.

Thus, for each given POI 134, the POI fingerprint module 140 maydetermine a plurality of timestamped LAN snapshots 123 that correspondto different times and/or different visits to the given POI 134. Theplurality of timestamped LAN snapshots 123 may show different variationsof LANs 135 (if any) and/or their associated metrics, asdetected/determined by the wireless devices 101 at the associated times.

As a non-limiting example, during the visit between 12:00 pm and 12:10pm on November 29 by the user 103 to the MacDonald's 500, the user's 103wireless device 102 may have reported two hundred timestamped LANsnapshots 123, if the wireless device 102 was reporting timestamped LANsnapshots 123 at a time frequency of ⅓ Hz. Understandably, thisparticular example of the frequency is given for illustration purposesonly, and may differ. The two hundred timestamped LAN snapshots 123 mayshow variations of, for example, signal strengths and different sets ofSSID's and MAC Addresses, as these may have been detected by thewireless device 102 over the ten minute period of the visit.

POI Fingerprints

Once the sets of LAN snapshots 123 have been determined for each givenPOI 134, the POI fingerprint module 140 may determine a POI fingerprint157 for each POI 134. To this end, for each given POI 134 the POIfingerprint module 140 may execute a statistics module that may averageout the variations in the various metrics represented by the timestampedLAN snapshots 123 corresponding to that given POI 134. This statisticsmodule may be implemented using any suitable known technology. The POIfingerprint module 140 may thereby produce a POI fingerprint 157 foreach given POI 134.

The POI fingerprint 157 of a given POI 134 may be indicative of thetypical LAN(s) 135, if any, that should be determined by a givenwireless device 101 at any given time from the location of the given POI134. In at least some non-limiting embodiments, a given POI fingerprint157 of a given POI 134 is a statistical average of all LAN snapshots 123that were determined to have been taken by a user while visiting thegiven POI 134.

In some non-limiting embodiments, the statistics module of the POIfingerprint module 140 may further determine additional statisticsand/or metrics that could be expected to be detected/determined by agiven wireless device 101 at any given time from the location of thegiven POI 134. In some such embodiments, the additional statisticsand/or metrics could form part of the POI fingerprint 157 of the givenPOI 134. For example, in some non-limiting embodiments, the additionalstatistics and/or metrics may include weighted percentages ofvisitors/users the wireless device(s) 101 of which detected given onesof the LANs at each given one of the POIs 134. As another example, theadditional statistics and/or metrics may include indications of signalstrength dispersion from the location of each given one of the POIs 134.

In some non-limiting embodiments, the POI fingerprint module 140generates multiple POI fingerprints 157 for each POI 134 in thegeographic area 136. In some such embodiments, each of the POIfingerprint 157 corresponds to a particular given timeframe on a givenday. In some such embodiments, each of the POI fingerprint 157corresponds to a particular given timeframe on a particular given day ofany given week. In some such embodiments, each of the POI fingerprint157 corresponds to a particular given timeframe on a particular givenday of a particular given week of the year. In other words, in at leastsome such non-limiting embodiments, the POI fingerprints 157 provide theLAN landscapes of the different POIs 134 as a function of time. In somesuch embodiments, the POI fingerprints 157 may be relatively moreaccurate.

Once the POI fingerprints 157 have been generated for the POIs 134 inthe geographical area 136, the server 118 may execute a featureengineering routine. The feature engineering aspect of the presenttechnology are described next.

Feature Engineering Module

Referring to FIG. 1 , in some non-limiting embodiments, the server 118further stores in its memory 120 and executes a feature engineeringmodule 152. The feature engineering module 152 is configured to executea feature engineering routine to determine those of the metrics of thePOI fingerprints 157 that distinguish visits to a first POI from visitsto a second POI. These metrics may be referred to as “more significantfeatures” that are more indicative of the fact of a visit to a givenPOI.

More particularly, in some non-limiting embodiments of the presenttechnology, the feature engineering module 152 determines the moresignificant features by comparing the POI fingerprints 157 to each otherto determine differences therebetween. The feature engineering module152 then applies a set of statistics to the determined differencesbetween the POI fingerprints 157 to determine trends therein and therebyderives the more significant features pertaining to each given locationin the geographical area 136. It is contemplated that any suitable setof statistics configured to find trends in data for example, could beused for this purpose. It is also contemplated that in some embodiments,the feature engineering module 152 and its associated steps could beomitted.

Training Dataset Generating Module

The server 118 further stores in its memory 120 and executes a trainingdataset generating module 154. In summary, and as described in detailbelow, the training dataset generating module 154 generates the trainingdataset that is then fed to a Machine Learning Algorithm (MLA) 158 totrain the MLA 158 to determine a fact of a visit to a POI 134 based on agiven LAN snapshot 123 as detected by a given wireless device 101.

To generate the training dataset, the training dataset generating module154 retrieves the identities of a previously-unused set of the wirelessdevices 101 associated with the geographical area 136 to obtainpreviously-unused geo-tracks therefrom. In some embodiments, thetraining dataset generating module 154 retrieves a previously-unusedgeo-tracks of at least some of the same wireless devices 101 that mayhave been used in generating the POI fingerprints 157. Previously-unusedwith respect to wireless devices 101 means that the wireless devices 101have not been used by the server 118 in generating the POI fingerprints157. Previously-unused with respect to geo-tracks means that thegeo-tracks have not been used by the server 118 in generating the POIfingerprints 157. In at least some cases, using previously-unusedwireless devices 101 and/or previously-unused geo-tracks thereofimproves the in-use accuracy of the MLA 158 that will be trained asdescribed in detail herein below.

This new, previously unused, set of timestamped geo-tracks is referredto as a training set of timestamped geo-tracks, with each given trainingset of timestamped geo-tracks including a plurality of timestampedgeo-locations of the corresponding wireless device 101. Those of thewireless devices 101 from which the training set of timestampedgeo-tracks have been obtained are referred to as training wirelessdevices 101.

For each given timestamped geo-location of a given training wirelessdevice 101, the training dataset generating module 154 retrieves fromthe LAN snapshot database 121 described above a timestamped LAN snapshot123 the timestamp of which corresponds to the timestamp of the giventimestamped geo-location. In other words, the training datasetgenerating module 154 determines what LANs and corresponding metrics, ifany, were detected by the given training wireless device 101 at thegiven timestamped geo-location.

The training dataset generating module 154 determines which of the POIs134, if any, are located within a pre-determined vicinity of each giventimestamped geo-location of each given training geo-track of eachtraining wireless device 101. In some non-limiting embodiments, thepre-determined vicinity is defined by a radius of 60 meters centeredabout the corresponding timestamped geo-location. In other non-limitingembodiments, the radius is different. It is contemplated that the radiuscould be pre-determined based on an accuracy of each given correspondingtimestamped geo-location. It is contemplated that the radius could bepre-determined for each given wireless device 101, both in the trainingphase and in an in use phase, based on the geo-locational accuracy ofthat given wireless device 101.

Where the training dataset generating module 154 determines that none ofthe POIs 134 are located within the pre-determined vicinity of a giventimestamped geo-location, the training dataset generating module 154labels the timestamped LAN snapshot 123 corresponding to the giventimestamped geo-location with a label indicating that none of the POIs134 is being visited.

On the other hand, where the training dataset generating module 154determines that at least one of the POIs 134 are located within thepre-determined vicinity of a given timestamped geo-location, thetraining dataset generating module 154 applies the set of heuristics, asdescribed herein above, to determine whether or not one of the at leastone of the POIs 134 is being visited by the user of the correspondingtraining wireless device 101 at the time corresponding to the timestampof the geo-location. The training dataset generating module 154 thenlabels the timestamped LAN snapshot 123 corresponding to the giventimestamped geo-location with a label indicating a result of thedetermination by the set of heuristics. Such labels may be referred toas training labels.

Thus, in some non-limiting embodiments, where the set of heuristicsreturns a positive determination for a given one of the POIs 134, thetraining dataset generating module 154 labels the timestamped LANsnapshot 123 corresponding to the given timestamped geo-location with atraining label indicating the name of the POI 134 determined as beingvisited. In some non-limiting embodiments, the training label mayinclude a name of the POI 134 being visited and a probability of the POI134 being visited. In some non-limiting embodiments, the training labelmay further include names of other POIs 134 in the pre-determinedvicinity and corresponding probabilities of those other POIs 134 beingvisited.

The dataset resulting from the above determination and labeling steps isreferred to as an MLA training dataset, and is used to train an MLA 158to determine/predict facts of visits by a given user of a given wirelessdevice 101 based on a LAN snapshot 123 detected by the given wirelessdevice 101 at a given point in time. The training phase is described indetail next.

MLA Training Module

The server 118 further stores in its memory 120 an MLA training module156, and an MLA 158, such as a neural network type MLA, or a GradientBoosting type MLA. The MLA training module 156 feeds the MLA trainingdataset to the MLA 158 to train the MLA 158.

More particularly, in the training phase, the MLA training module 156may input into the MLA 158 each given training LAN snapshot 123 and maydesignate the training label corresponding to the training LAN snapshot123 as the corresponding output of the MLA 158. In this sense, eachgiven training LAN snapshot 123 provides a training set of local areacommunication features for the MLA 158.

In some non-limiting embodiments, during the training phase, the MLAtraining module 156 conditions the MLA 158 to weigh the more significantfeatures of each given training LAN snapshot 123 more heavily in makinga determination of a visit than other features of the given training LANsnapshot 123. It is contemplated that in some embodiments, thisconditioning step could be omitted.

In some embodiments, the MLA 158 is trained to output a name or otheridentifier of a given POI 134 being visited for positive determinations,and an indication that none of the POIs 134 is being visited fornegative determinations.

In some embodiments, the MLA 158 may be trained to output a ranked listof identified POIs 134, in which list each of the POIs 134 is providedwith an indication of a probability, the probability indicating that acorresponding user is visiting that particular one of the POIs 134. Thelist may be ranked from a most-probably visited one of the POIs 134 to aleast-probably visited one of the POIs 134.

Once the MLA 158 is sufficiently trained and with the server 118 andsystem 100 structure described above, various methods can now bepracticed. These methods are described next.

Method of Determining a Visit to a POI

Now referring to FIG. 6 , in some non-limiting embodiments of thepresent technology, there is provided a method 600 of determining a factof a visit of a user to a point of interest (POI) 134, the user using awireless device having a local area communication module 109, the POI134 being in a location, the location being associated with a local areanetwork (LAN), the LAN including at least one wireless access point 137broadcasting a wireless signal.

In some non-limiting embodiments, the method 600 is executed at theserver 118 having a processor 122 and a non-transient memory 120communicatively coupled to the processor 122, the non-transient memory120 storing processor-executable instructions thereon for executing themethod 600. One example of a server suitable for executing the method600 is the server 118, as described above. Accordingly, for simplicityand not to be limiting, the method 600 will be described with regard tothe server 118.

Step 602—receiving during an in-use phase, at the server, a wirelessdevice identifier of the wireless device.

In one embodiment, the method 600 starts at step 602, which includesreceiving during an in-use phase, at the server 118, a wireless deviceidentifier 206 of the wireless device 101 for which the method 600 willdetermine a fact of a visit to a given POI 134.

As a non-limiting example, and referring to FIG. 5 , let's assume thatthe server 118 receives the wireless device identifier 206 of a wirelessdevice 166 that is being used by a user 162. As described above, oneexample of the wireless device identifier 206 is the user device ID 206.

Let's assume that a given POI 134 for which a fact of a visit by theuser 162 is to be determined is the MacDonald's 500 in the geographicalarea 136. It is noted that to maintain simplicity, the user 162 is shownwith the same figurine as the user 103 described above. It will beappreciated, however, that in the present example the user 162 isdifferent from the user 103 described above.

Step 604—determining during the in-use phase, at the server based on thewireless device identifier, a set of local area communication featuresassociated with the local area communication module of the wirelessdevice of the user, the set of local area communication features havingoccurred at a given time.

In one embodiment, the method 600 may proceed with determining, duringthe in-use phase, at the server 118 based on the wireless deviceidentifier 206, a set of local area communication features associatedwith the local area communication module 109 of the wireless device 166of the user 162, the set of local area communication features havingoccurred at a given time.

In some non-limiting embodiments, for each occurrence of an execution ofthe method 600, the given time is the time as of which the method 600 isto determine whether or not a given user is visiting a given POI 134.

Let's assume that the user 162, having the wireless device 166, entersthe MacDonald's 500 on Dec. 4, 2018, at 10:43 am Eastern Time (US &Canada), as shown in FIG. 5 .

For this given time, in some non-limiting embodiments, the server 118retrieves based on the wireless device identifier 206, the LAN snapshot123 as detected by the local area communication module 109 (in thepresent non-limiting example, a Wi-Fi module) of the wireless device 166at the given time.

Thus, in the present example, the LAN snapshot 123 retrieved by theserver 118 thus includes the LANs 135 as detected by the wireless device166 from inside the MacDonald's 500 at 10:43 am Eastern Time on Dec. 4,2018. As an example, at 10:43 am on Dec. 4, 2018 the wireless device 166at this time could have detected: 1) the LAN 135 that belongs to theMacDonald's 500 itself, 2) the LAN 135 in the residence 501 that islocated in proximity to the South East of the MacDonald's 500; and 3) aWi-Fi 172 provided by the Petro-Canada gas station 508 located nearby tothe North East.

In the present non-limiting embodiment and example, the wireless device166 detected, for each of the above Wi-Fi's at least an SSID, a MACaddress of the corresponding wireless access point 137 and/or LAN 135,and a signal strength of the wireless signal of the correspondingwireless access point 137. This determined data thus forms at least partof the set of local area communication features for input to the MLA158.

Step 606—inputting during the in-use phase, by the server into a MachineLearning Algorithm (MLA) as an in-use input, the set of local areacommunication features of the local area communication module of thewireless device, the MLA having been trained during a training phasebased on a training dataset associated with the POI, the training phasehaving preceded the in-use phase, a given training object of thetraining dataset having been generated by: determining during thetraining phase, at least one geo-location of a training wireless deviceof a training user, the at least one geo-location having a timeassociated therewith that occurred before the given time, the POI beingin a pre-determined vicinity of the at least geo-location; determiningduring the training phase a set of training local area communicationfeatures that the training wireless device had at the time associatedwith the at least one geo-location, the set of training local areacommunication features including an indication of the training wirelessdevice having detected from the at least one geo-location the wirelesssignal of the at least one wireless access point of the LAN, thetraining wireless device having: a training wireless device identifier,and a training local area communication module.

In one embodiment, the method 600 may proceed with inputting, during thein-use phase, by the server 118 into a Machine Learning Algorithm (MLA),such as the MLA 158, as an in-use input, the set of local areacommunication features (SSIDs, MAC Addresses, signal strengths, etc.)determined by the wireless device 166 of the user 162 at the first giventime.

In the present part of the example, the set of local area communicationfeatures inputted into the MLA 158 therefore includes at least the SSID,the MAC address, and the signal strength of each of: 1) the LAN 135 thatbelongs to the MacDonald's 500, 2) the LAN 135 in the residence 501; and3) the Wi-Fi 172 provided by the Petro-Canada gas station 508.

It is contemplated that in some embodiments, only the SSID and thesignal strength could be used. It is also contemplated that in someembodiments, only the MAC address and the signal strength could be used.In some non-limiting embodiments, POIs 134 within a pre-defined vicinityof the geo-location associated with each given set of local areacommunication features are determined, similar to how this determinationwas described above.

In some non-limiting embodiments, at this stage, the MLA 158 had beencorrespondingly trained as described above during a training phase basedon a training dataset associated at least with the given POI 134, and insome cases with all POIs 134 in the geographical area 136. The creationof the training dataset was described above, and will not be repeatedhere in detail.

Step 608—receiving during the in-use phase, by the server from the MLAas an in-use output based on the set of local area communicationfeatures of the local area communication module of the wireless device,an indication of whether the user visited the POI at the given time.

In one embodiment, the method 600 may proceed with receiving during thein-use phase, by the server 118 from the MLA 158 as an in-use outputbased on the set of local area communication features of the local areacommunication module 109 of the wireless device 166, an indication ofwhether the user 162 visited the POI 134 at the given time.

In the present example, the MLA 158 therefore outputs an indication thatthe user 162 is visiting the MacDonald's 500. In some embodiments, theindication includes a name and/or address and/or other identifier(s) ofthe particular POI 134 determined to be visited. Thus, in the aboveexample, the indication may simply be: MacDonald's 500, Address: XYZ.

In some embodiments, the MLA 158 outputs a ranked list of potential POIs134 being visited, of the POIs 134 in a pre-determined vicinity of theuser's 162 geo-location. In some embodiments, the rank list includes a“Yes” or a “No” indication next to each of the POIs 134 on the list, andthe POIs 134 that are more likely to be visited are listed higher thanthe rest of the POIs 134 on the list. In some such embodiments, in theabove example, the ranked list may indicate: 1) MacDonald's 500, “YES”,Address: XYZ.; 2) Petro-Canada, “NO”, Address: XYZ.

In other embodiments, the ranked list may indicate a probability of avisit to each of the POIs 134 on the ranked list. Thus, in the aboveexample, the ranked list may indicate: 1) MacDonald's 500, 86%(probability of visit), Address: XYZ; 2) Petro-Canada, 14% (probabilityof visit), Address: XYZ.

Understandably, the steps 602 to 608 of the method 600 may be executedwith respect to each geo-location reported by the user's 162 wirelessdevice 166. Each new LAN snapshot 123 corresponding to each new reportedgeo-location may be inputted into the MLA 158 as a new set of local areacommunication features.

The above example is given with respect to tracking POI visits of asingle user. However, the method 600 may also be used to track anddetermine POI visits of a plurality of users to any one of the POIs 134in the geographical area 136.

Yet other embodiments, applications and implementations of the method600 are likewise contemplated. For example, with reference to FIG. 7 ,there is shown a method 700 that is executed using the method 600.

Method of Determining a Conversion Rate of a Targeted Message

More particularly, the method 700 is a method of determining aconversion rate of a targeted message associated with a POI. As with themethod 600, the method 700 may be executed at a server having aprocessor and a non-transient memory communicatively coupled to theprocessor, the non-transient memory storing processor-executableinstructions thereon for executing the method.

Similar to the method 600, one example of a server suitable forexecuting the method 700 is the server 118. Therefore, the method 700will be described herein next with regard to the server 118 forillustration.

Step 702—determining during an in-use phase, by the server, a pluralityof users that have been exposed to a targeted message associated withthe POI within a first pre-determined time period, each user of theplurality of users using a wireless device having a local areacommunication module and a wireless device identifier associated withthe local area communication module.

In one embodiment, the method 700 starts at step 702, which includesdetermining during an in-use phase, by the server 118, a plurality ofusers that have been exposed to a targeted message associated with thetarget POI within a first pre-determined time period, each user of theplurality of users using a wireless device 101 having a local areacommunication module 109 and a wireless device identifier 206 associatedwith the local area communication module 109.

Referring also to FIG. 5 , let's assume that the targeted message forwhich the method 700 will determine a conversion rate is shown on abillboard 150 and the targeted message directs viewers to theMacDonald's 500 (i.e. the target POI 134 in this example). Let's alsoassume that the pre-determined time period is a given day of a givenweek.

Further, let's assume that users U1 to Un, shown in FIG. 5 , aredetermined by the server 118 to have been exposed to the targetedmessage displayed on the billboard 150 at various times during the givenday of the given week.

In some non-limiting embodiments, this determination step is executed bythe server 118 using the technology described in U.S. patent applicationSer. No. 16/739,668, filed concurrently with the present application bythe same applicant. It is contemplated that other methods foridentifying users exposed to targeted messages could also be used.

Step 704—receiving during the in-use phase, at the server based on thewireless device identifiers of the wireless devices of the plurality ofusers, a plurality of sets of local area communication features of thelocal area communication modules of the wireless devices.

Once the time(s) of exposure of each of the users U1 to Un to thetargeted message on the billboard 150 has been retrieved/determined bythe server 118, the method 700 may proceed with receiving, during thein-use phase, at the server 118 based on the wireless device identifiers206 of the wireless devices 101 of the plurality of users U1 to Un, aplurality of sets of local area communication features of the local areacommunication modules 109 of the wireless devices 101.

In some embodiments, the plurality of sets of local area communicationfeatures are the LAN snapshots 123 generated by the wireless devices 101of the users U1 to Un which are determined by the server 118 to havebeen generated by the wireless devices 101 at times occurring after eachof the respective users U1 to Un was exposed to the targeted message onthe billboard 150.

Some of the users U1 to Un may ignore the targeted message and moveabout the geographical area 136 without visiting the MacDonald's 500.Some of the users U1 to Un may be convinced by the targeted message andwill actually make their way to the MacDonald's 500. The timestamped LANsnapshots 123 from each of these user's devices that are generated“post-exposure” to the targeted message will reflect these movements andvisits when inputted into the MLA 158.

Steps and methods for determining the LAN snapshots 123 were describedabove and are therefore not described again in detail.

Step 706—inputting, by the server into the MLA as described above as anin-use input, the plurality of sets of local area communication featuresof the wireless devices.

Once the LAN snapshots 123 are retrieved/determined by the server 118,the method 700 may proceed with inputting LAN snapshots 123, by theserver 118 into the MLA 158, as an in-use input.

Step 708—receiving, by the server from the MLA as an in-use output basedon the plurality of sets of local area communication features of thewireless devices, a plurality of indications, a given indication of theplurality of indications being associated with a given user of theplurality of users and indicating whether the given user visited the POIwithin a second pre-determined time period after having been exposed tothe targeted message.

The method 700 may proceed with receiving, by the server 118 from theMLA 158 as an in-use output based on the plurality of LAN snapshots 123(sets of local area communication features) of the wireless devices 101,a plurality of indications. The indications may be implemented in anysuitable form, such as one or more suitable data packets for eachindication. The indications may be characterized as follows.

A given indication of the plurality of indications may be associatedwith a given user of the plurality of users U1 to Un and may indicatewhether the given user visited the MacDonald's 500 within a secondpre-determined time period after having been exposed to the targetedmessage on the billboard 150.

For the sake of the present non-limiting example, let's assume that thesecond pre-determined time period is equal to five hours. Hence, at thisstep, each given indication is indicative of whether or not a given oneof the users U1 to Un visited the MacDonald's 500 within five hours ofhaving been exposed to the targeted message on the billboard 150.Understandably, the five hours is just an example and the value of thesecond pre-determined time period could be different.

Step 710—determining during the in-use phase, by the server, based onthe plurality of indications, a conversion rate parameter indicating theconversion rate of the targeted message, the conversion rate being apercentage of the plurality of users that have visited the POI withinthe second pre-determined time period after having been exposed to thetargeted message.

In one embodiment, the method 700 may proceed by determining, during thein-use phase, by the server 118, based on the plurality of indications,a conversion rate parameter indicating the conversion rate of thetargeted message. In some non-limiting embodiments, the conversion rateis a percentage of the plurality of users that have visited a POI 134targeted by a given targeted message within the second pre-determinedtime period after having been exposed to the targeted message.

Thus, in the present non-limiting example, the conversion rate is apercentage of the users U1 to Un that visited the MacDonald's 500 withinthe five hours following being exposed to the targeted message on thebillboard 150.

While the method 700 is described above with respect to one particularexample of a time, a particular a POI, and a particular offline targetedmessage, it is contemplated that the method 700 could be used todetermine conversion rates for many different POIs, over different timeframes, and for different types of targeted messages. It is contemplatedthat the targeted messages could be offline targeted messages, onlinetargeted message, and/or combinations thereof.

Method of Determining Place Visits Lift

The present technology further provides for an additional particular wayof using the method 600. Namely, the present technology provides for amethod 800 of determining a Place Visits Lift (PVL) metric, or simplyPVL.

The present technology relating to determining the PVL has beendeveloped with the developers' appreciation that when targeted messagesare directed to an offline site, such as a POI (see for example POIs 134in FIG. 5 ), it is difficult to determine an efficacy of the targetedmessages in directing users that were exposed to the targeted messagesto the POIs that the targeted messages target. More particularly, it hasbeen appreciated by the developers that the difficulty with determiningan efficacy of targeted messages in increased at least in some caseswhen the targeted messages are online but the POIs that the targetedmessages are directed to are offline (see for example, physical POIs 134in FIG. 5 ).

In some non-limiting embodiments, determining PVL according to thepresent technology allows to quantify an efficacy of a given targetedmessage in directing users to a particular POI to which the giventargeted message relates. In an aspect, increases in PVL may be said tobe indicative of a measure of how much more users were persuaded by agiven one or more targeted messages to visit the particular POIs thatthe targeted messages relate to, in comparison with users who were notexposed to the given one or more targeted messages.

Monitoring the PVL for given one or more targeted messages according tothe present technology therefore allows to determine which one(s) of thegiven one or more targeted messages are under-performing, performing, orover-performing. Accordingly, the present technology may allowprovider(s) of the given one or more targeted messages to, for example,remove under-performing targeted messages from being displayed to usersand may thereby allow for resources associated with displaying suchtargeted messages to be reduced and/or saved. Other uses of PVLdetermined according to the present technology are likewisecontemplated.

A particular non-limiting embodiment of the method 800 for determiningPVL according to the present technology is shown in FIG. 8 , referred tonext. The method 800 is executable at a server having a processor and anon-transient memory communicatively coupled to the processor, thenon-transient memory storing processor-executable instructions thereonfor executing the method. An example of a suitable server is server 118,described above. Thus, for simplicity and not to be limiting, the method800 will be described and illustrated with regard to the server 118.

Step 802—determining, by the server, a visitors_site parameter, thevisitors_site parameter being equal to a number of users that both: a)were exposed to a targeted message directing users to a point ofinterest (POI), and b) visited the POI after having been exposed to thetargeted message.

In one embodiment, the method 800 starts at step 802, which includesdetermining, by the server 118, a visitors_site parameter.

In some non-limiting embodiments, the visitors_site parameter is equalto a number of users that were to have both: a) been exposed to atargeted message directing users to a point of interest (POI), and b)visited the POI after having been exposed to the targeted message.

As a non-limiting example, let's say the targeted message is anindication, posted on the billboard 150 shown in FIG. 5 , that a deal isavailable for one of the products offered at the MacDonald's 500 in thegeographical region 136. According to this non-limiting example then,the POI, or the target POI, of the targeted message is the MacDonald's500 shown in FIG. 5 .

In this non-limiting example, the facts and times of each of the usersbeing exposed to the targeted message may be determined using thetechnology described in the U.S. patent application Ser. No. 16/739,668.

Further in this non-limiting example, the facts of the visits, or simplythe visits, and the associated times of the visits of each of the(identified) users may be determined according to the method 600described above.

Step 804—determining, by the server, a bypassers_site parameter, thebypassers_site parameter being equal to a number of users that did notvisit the POI after having been exposed to the targeted message.

In some non-limiting embodiments, the method 800 may proceed bydetermining, by the server, a bypassers_site parameter, thebypassers_site parameter being equal to a number of users that did notvisit the POI after having been exposed to the targeted message.

In this non-limiting example, for each given user of one of the wirelessdevices 101 that was determined to have been exposed to the targetedmessage displayed on the billboard 150, the method 600 described abovemay be used to monitor the given user's activity for at least apre-determined period of time after the exposure.

More particularly, the method 600 may be used to determine whether atany given point in time (or at multiple given points in time) during thepre-determined period of time after the exposure, the user visited theMacDonald's 500.

Step 806—by the server, a visitors_non-site parameter, thevisitors_non-site parameter being equal to a number of users that werenot exposed to the targeted message but visited the POI.

In one embodiment, the method 800 may proceed by determining avisitors_non-site parameter. In some non-limiting embodiments, thevisitors_non-site parameter is equal to a number of users that were notexposed to the targeted message but visited the POI.

Similar to above, the facts and times of visits may be determined by themethod 600, and exposures to the targeted message may be determinedusing the technology described in the U.S. patent application Ser. No.16/739,668.

Step 808—determining, by the server, a bypassers_non-site parameter, thebypassers_non-site parameter being equal to a number of users that werenot exposed to the targeted message and did not visit the POI.

In one embodiment, the method 800 may proceed by determining abypassers_non-site parameter. In some non-limiting embodiments, thebypassers non-site parameter is equal to a number of users that were notexposed to the targeted message and did not visit the POI.

Similar to above, the facts and times of visits may be determined by themethod 600, and lack of exposures to the targeted message may bedetermined using the technology described in the U.S. patent applicationSer. No. 16/739,668.

Step 810—determining, by the server, the PVL metric.

Once all of the above parameters are determined, the method 800 mayproceed to determining the PVL metric based on the determinedparameters.

According to the present technology, the PVL metric is determined by theserver 118, based on the parameters described above, according to thefollowing formula:

$\begin{matrix}{{{PVL} = {{\frac{{visitors\_ site}/{bypassers\_ site}}{{visitors\_ non}\text{-}{{site}/{bypassers\_ non}}\text{-}{site}}*100\%} - {100\%}}},} & {{Formula}\mspace{14mu} 1}\end{matrix}$

where, each of the parameters has the corresponding one of thedefinitions given above in the earlier steps of the method 800.

It is contemplated that in some embodiments, some but not all of theabovementioned parameters may be determined according to the presenttechnology. While the particular targeted message used to illustratemethod 800 is an offline targeted message, in some non-limitingembodiments, the method 800 is used to determine PVL for online targetedmessages.

Modifications and improvements to the above-described implementations ofthe present technology may become apparent to those skilled in the art.The foregoing description is intended to be exemplary rather thanlimiting.

While particular orders of methods steps have been set out herein above,it is contemplated that other orders of method steps and/or additionaland/or alternative method steps could be used to carry out the methodsdescribed herein.

Modifications and improvements to the above-described implementations ofthe present technology may become apparent to those skilled in the art.The foregoing description is intended to provide specific non-limitingimplementations of the present technology.

The invention claimed is:
 1. A method of determining a conversion rateof a targeted message associated with a point of interest (POI), the POIbeing in a location, the location being associated with a local areanetwork (LAN), the LAN including at least one wireless access pointbroadcasting a wireless signal, the method being executed at a serverhaving a processor and a non-transient memory communicatively coupled tothe processor, the non-transient memory storing processor-executableinstructions thereon for executing the method, the method comprising:determining during an in-use phase, by the server, a plurality of usersthat have been exposed to the targeted message associated with the POIwithin a first pre-determined time period, a given user of the pluralityof users using a respective wireless device having a local areacommunication module and a wireless device identifier associated withthe local area communication module; receiving during the in-use phase,at the server based on the wireless device identifiers of the wirelessdevices of the plurality of users, a plurality of sets of local areacommunication features of the local area communication modules of thewireless devices; training a Machine Learning Algorithm (MLA) during atraining phase based on a training dataset associated with the POI, thetraining phase having preceded the in-use phase, a given training objectof the training dataset having been generated by: determining at leastone geo-location of a training wireless device of a training user, theat least one geo-location having a time associated therewith thatoccurred before the given time, the POI being in a pre-determinedvicinity of the at least geo-location; determining a set of traininglocal area communication features that the training wireless device hadat the time associated with the at least one geo-location, the set oftraining local area communication features including an indication ofthe training wireless device having detected from the at least onegeo-location the wireless signal of the at least one wireless accesspoint of the LAN, the training wireless device having: a trainingwireless device identifier, and a training local area communicationmodule, applying during the training phase a set of heuristics to a userprofile associated with the training user to generate a training labelindicative of whether the training user visited the POI at the timeassociated with the at least one geo-location, and thereby generatingduring the training phase the given training object having the set oftraining local area communication features and the training labelassociated therewith; inputting, by the server into the MLA as an in-useinput, the plurality of sets of local area communication features of thewireless devices; receiving, by the server from the MLA as an in-useoutput based on the plurality of sets of local area communicationfeatures of the wireless devices, a plurality of indications, a givenindication of the plurality of indications being associated with thegiven user of the plurality of users and indicating whether the givenuser visited the POI within a second pre-determined time period afterhaving been exposed to the targeted message; determining during thein-use phase, by the server, based on the plurality of indications, aconversion rate parameter indicating the conversion rate of the targetedmessage, the conversion rate being a percentage of the plurality ofusers that have visited the POI within the second pre-determined timeperiod after having been exposed to the targeted message.
 2. The methodof claim 1, wherein the user profile comprises a user specific portionand a device specific portion.
 3. The method of claim 2, wherein the setof heuristics comprises a first subset of heuristics applicable to theuser specific portion, and the first subset of heuristics is forexecuting a determination that the training user interacted with aresource associated with the POI, the resource including at least one ofa telephone number associated with the POI, and a website associatedwith the POI.
 4. The method of claim 3, wherein the first subset ofheuristics is further for executing a determination that the traininguser interacted with a web resource and the interaction with the webresource included the training user executing a web search associatedwith the POI.
 5. The method of claim 4, wherein the web resource is amap service and the interaction was a search in the map service for thePOI, the search having been executed by the training user.
 6. The methodof claim 2, further comprising determining, based on the trainingwireless device identifier: a set of time-stamped geo-tracked locationsof the training wireless device, the set of time-stamped geo-trackedlocations being indicative of locations where the training wirelessdevice was located over a pre-determined period of time, and wherein:the set of heuristics comprises a second subset of heuristics applicableto the device specific portion, and the second subset of heuristicsbeing for executing a determination based on the set of time-stampedgeo-tracked locations that the training user moved to within thepre-determined vicinity of the location of the POI.
 7. The method ofclaim 6, wherein the second subset of heuristics is further forexecuting a determination that the training user was exposed to thetargeted message associated with the POI before having moved to withinthe pre-determined vicinity of the location of the POI.
 8. The method ofclaim 7, wherein the targeted message is an online targeted message. 9.The method of claim 7, wherein the targeted message is an offlinetargeted message.
 10. The method of claim 7, wherein the second subsetof heuristics is further for executing a determination that the traininguser moved to within the pre-determined vicinity of the location of thePOI within a pre-determined time limit after having been exposed to thetargeted message.
 11. The method of claim 10, wherein the pre-determinedtime limit is less than five hours.
 12. The method of claim 6, whereinthe pre-determined vicinity is defined by a radius of five hundredmeters away from the location of the POI.
 13. The method of claim 6,wherein the pre-determined vicinity is defined by a radius of onehundred and fifty meters away from the location of the POI.
 14. Themethod of claim 6, wherein the second subset of heuristics is furtherfor executing a determination that the training wireless deviceconnected, via the training communication module, to the local areanetwork (LAN), and wherein the LAN is associated with the POI.
 15. Themethod of claim 14, wherein the location is associated with a pluralityof LANs, the plurality of LANs including the LAN, and wherein thetraining local area communication features include at least one of: aService Set ID (SSID) of each LAN of the plurality of LANs, a MACAddress of a wireless access point associated with each LAN of theplurality of LANs, and a signal strength of the wireless access pointassociated with each LAN of the plurality of LANs.
 16. The method ofclaim 1, further comprising executing a feature engineering routineprior to the training phase, the feature engineering routine determininga plurality of features that are part of the set of training local areacommunication features, the plurality of features to be weighed moreheavily by the MLA than the rest of the set of training local areacommunication features during the training phase.
 17. Acomputer-implemented system for determining a conversion rate of atargeted message associated with a point of interest (POI), the POIbeing in a location, the location being associated with a local areanetwork (LAN), the LAN including at least one wireless access pointbroadcasting a wireless signal, the system comprising a server having aprocessor and a non-transient memory communicatively coupled to theprocessor, the non-transient memory storing processor-executableinstructions which when executed cause the processor to: determineduring an in-use phase, by the server, a plurality of users that havebeen exposed to the targeted message associated with the POI within afirst pre-determined time period, a given user of the plurality of usersusing a respective wireless device having a local area communicationmodule and a wireless device identifier associated with the local areacommunication module; receive during the in-use phase, at the serverbased on the wireless device identifiers of the wireless devices of theplurality of users, a plurality of sets of local area communicationfeatures of the local area communication modules of the wirelessdevices; train a Machine Learning Algorithm (MLA) during a trainingphase based on a training dataset associated with the POI, the trainingphase having preceded the in-use phase, to generate a given trainingobject of the training dataset the processor being configured to:determine at least one geo-location of a training wireless device of atraining user, the at least one geo-location having a time associatedtherewith that occurred before the given time, the POI being in apre-determined vicinity of the at least geo-location; determine a set oftraining local area communication features that the training wirelessdevice had at the time associated with the at least one geo-location,the set of training local area communication features including anindication of the training wireless device having detected from the atleast one geo-location the wireless signal of the at least one wirelessaccess point of the LAN, the training wireless device having: a trainingwireless device identifier, and a training local area communicationmodule, apply during the training phase a set of heuristics to a userprofile associated with the training user to generate a training labelindicative of whether the training user visited the POI at the timeassociated with the at least one geo-location, and thereby generatingduring the training phase the given training object having the set oftraining local area communication features and the training labelassociated therewith; input, by the server into the MLA as an in-useinput, the plurality of sets of local area communication features of thewireless devices; receive, by the server from the MLA as an in-useoutput based on the plurality of sets of local area communicationfeatures of the wireless devices, a plurality of indications, a givenindication of the plurality of indications being associated with thegiven user of the plurality of users and indicating whether the givenuser visited the POI within a second pre-determined time period afterhaving been exposed to the targeted message; determine during the in-usephase, by the server, based on the plurality of indications, aconversion rate parameter indicating the conversion rate of the targetedmessage, the conversion rate being a percentage of the plurality ofusers that have visited the POI within the second pre-determined timeperiod after having been exposed to the targeted message.